Development of a phenomenological Semi-Predictive NOx Model for a High Performance Hydrogen-fuelled Spark Ignition Engine

Nowadays, hydrogen represents a promising alternative to fossil fuels in its use as an energy carrier for internal combustion engines. The absence of carbon in hydrogen is a key factor in reducing greenhouse gas emissions and unburned hydrocarbons. However, hydrogen combustion unfortunately generates nitrogen oxides (NOx), pollutants that result from the oxidation reaction of molecular nitrogen present in the intake air at high temperatures. In this framework, this thesis aims to develop a semi-predictive phenomenological model for estimating NOx emissions in a hydrogen-powered engine. Starting from the anylisis of experimental data, a mathematical calculation model to predict NOx as a function of the main engine parameters that most influence emission variations will be exposed. Subsequently, the model will be integrated into a complete 0D/1D engine model in a virtual test bench using GT-SUITE software. Finally, once the model has been calibrated with experimental results, a virtual calibration will be performed across the entire engine map.